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My core research interest is developing high-salable knowledge-based complex event processing engines which utilize knowledge representation methods for the domain of event processing to improve the quality of event processing.

We are collecting data at a high-speed and from various resources containing valuable insights. The data value is high as long as it is fresh enough to be useful. We need data processing systems that can analyze high throughput data streams and extract update information in real-time so that it can be utilized for time critical business decisions. About a decade ago, researchers and engineers started developing data stream processing systems in answer to the constant growth of data streams in business. In comparison, today’s data streams are gigantic in volume, arriving at breakneck speed and divergent, making them difficult to understand and analyze. In addition, real-time analytics of data stream is becoming increasingly crucial for a wide range of use cases in industry and research, like sensor data, financial data, web data, scientific satellite data, so that providing continuous accurate or approximate information is even more important than before. Processing data streams has received a lot of attention in different computer science communities, i.a., databases, distributed systems, logic and rule-based systems.

My research has focused on the challenges of real-time information extraction from big data streams. I have been working at the intersection of the research topics ​ database technologies, data mining, logic and logical knowledge representation. On the database side, I am interested in solving problems related to real-time data processing/complex event processing, real-time data mining of streaming data, while on the topics of logical/knowledge representation, I am concerned with knowledge representation models for complex events and situations.

At Rice University I was a member of Pliny research project. Within the Pliny project I was working on Pliny Database (PDB) system which is a Cluster Compute Platform. You can find more details on our initial research paper and poster.

At Free University Berlin I was involved in the following research projects:

Corporate Smart Content, our sub-project Smart Content Enrichment funded by the German Federal. Ministry of Education and Research (BMBF). I worked on “Semantic Mining of Complex Event Patterns from Coprorate Data Streams“.Abstract: “In this research, we address the problem of automated extraction of patterns for detection of complex events. The existing approaches for the pattern detection are primarily dealing with syntactical processing of event sequences to detect complex patterns only based on the sequences of event happening. As an extension of the existing approaches for pattern mining, we investigate the usage of ontological background knowledge to be able to extract complex event patterns based on the relations of patterns to the resources in the background knowledge.”

Corporate Semantic Web funded by the German Federal. Ministry of Education and Research (BMBF). I was involved in multiple subprojects but more was focused on my research on Semantic Complex Event Processin.Abstract: “One of the critical success factors of event-driven systems is the capability of detecting complex events from simple and ordinary event notifications. Complex events which trigger or terminate actionable situations can be inferred from large event clouds or event streams based on their event instance sequence, their syntax and semantics. Using semantics of event algebra patterns defined on top of event instance sequences for event detection is one of the promising approaches for detection of complex events. The developments and successes in building standards and tools for semantic technologies such as declarative rules and ontologies are opening novel research and application areas in event processing. One of these promising application areas is semantic event processing. “

“Digipolis” project funded by the German Federal. Ministry of Education and Research (BMBF) under grant number 03WKP07B. I worked on a “A Swarm-based Semantic Storage Service”.
Abstract: The amount of data handled by semantic applications is expected to increase over a level manageable by available storage systems. Distributed semantic storage solutions are a promising way to increase storage capacity, but current approaches often rely on static network structures. We are in the process of developing a Self-Organized Semantic Storage Service using swarm intelligence to overcome the limitations in storage capacity and network dynamics.

TripCom (Triple Space Communication) project was a Strategic Research Project (STREP) funded by the European Commission under FP6. I was involved in project workpackage 2 and worked on building a distributed middelware for semantic data.